In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

Fraud detection system

Our school prides itself on providing the best opportunities for each child. The academic achievements have been and continue to be well above Western Australian and National averages, while the pastoral care programs that operate in the school teach children the specific social and emotional skills they need in order to develop and maintain friendships and a strong sense of selfworth. Students have a strong sense of pride in the school and display significant support for each other. Student success is valued, supported and celebrated by all.

Annual Report 2012 -個人別教育研究報告-

IEEE Transactions on Neural Networks

/pdf/utilization-of-duckweed-lemna-paucicostata-in-least-cost-6s65won5w3.pdf

Utilization of Duckweed (Lemna paucicostata) in Least-cost Feed Formulation for Broiler Starter: A Linear Programming Analysis

Cyber security is among the most complex and rapidly evolving issues and has been the focus of present day organizations. Cyber security risk management is the process of managing or reducing potentially harmful and uncertain events that posse as threats to cyber security. It involves looking at what could go wrong on the cyber space and deciding on ways to prevent or minimize their occurrences or effects. One of the prominent cyber security risk management techniques is the Game Theoretic Approach (GTA), which focuses on the use of resources, internal controls, information sharing, technical improvements, behavioral or organizational scale-ups and cyber insurance for cyber risk management. This paper presents a review of game theoretic-based model for cyber security risk management. Specifically, issues on modeling, some related works and significance of game theoretic approach to cyber security risk management are presented. Findings from the review revealed the peculiarities and specificity of each model. It is also revealed that the models are just evolving and require much improvement. Keywords: Cyber Security, Risk Management, Game Theory, Model

/pdf/a-review-of-game-theory-approach-to-cyber-security-risk-mljo4c2154.pdf

A review of game theory approach to cyber security risk management

Unexpectedly, the level of patronage being experienced in Global System for Mobile Communication (GSM) in Nigeria is overwhelming. This is as a result of freedom of calling from anywhere at anytime and clarity of the voice enjoyed in GSM since it is on a digital technology platform. This has brought a lot of congestion in the network resulting in poor services by the operators. This research has developed a management algorithm for the management of the congestion ex- perienced in the GSM network in Nigeria. It explores the use of Erlang-B in determining the appropriate probability level for some range of subscribers. Thereafter, when there is congestion, block time sharing, dynamic allocation without time slicing, dynamic allocation time slicing with signal sensing, frequently recent call allocation, and priority allocation algorithms were developed to manage the congestion. Furthermore, a hybrid algorithm was developed that integrates all the algorithms to- gether in other to manage the congestion considering all the strengths and constraints of each algorithm. If the recommended congestion management algorithm is followed comprehensibly, the congestion problem on the GSM network will be reduced drastically.

/pdf/development-of-models-for-managing-network-congestion-on-4wc0p14pya.pdf

Development of Models for Managing Network Congestion on Global System for Mobile Communication (GSM) in Nigeria

The unpredictable cyber attackers and threats have to be detected in order to determine the outcome of risk in a network environment. This work develops a Bayesian network classifier to analyse the network traffic in a cyber situation. It is a tool that aids reasoning under uncertainty to determine certainty. It further analyze the level of risk using a modified risk matrix criteria. The classifier developed was experimented with various records extracted from the KDD Cup'99 dataset with 490,021 records. The evaluations showed that the Bayesian Network classifier is a suitable model which resulted in same performance level for classifying the Denial of Service (DoS) attacks with Association Rule Mining while as well as Genetic Algorithm, the Bayesian Network classifier performed better in classifying probe and User to Root (U2R) attacks and classified DoS equally. The result of the classification showed that Bayesian network classifier is a classification model that thrives well in network security. Also, the level of risk analysed from the adapted risk matrix showed that DoS attack has the most frequent occurrence and falls in the generally unacceptable risk zone.

Risk analysis in cyber situation awareness using Bayesian approach

The population of students all over the world is growing without a proportionate increase in teaching/learning resources/infrastructure. There is also much quest for learning in an environment that provides equal opportunities to all learners. The need to provide an equal opportunity learning environment that will hitherto improve the system of education globally has therefore become imperative. Based on our findings, a mathematical model Web-based Virtual Classroom system (WebVCS) was developed to provide a viable medium through which sound education can be offered in tertiary institutions that can carter for varieties of learners irrespective of their abilities, dispositions and geographical locations. Our system model is developed based on active learning approach that adopts blended learning theory (Constructivist-Cognivist learning approach), incorporating e-pedagogy that supports collaboration among participants in the web-based Virtual learning environment. The key objects used in creating the WebVCS model are: Courses, Students, Instructors and Learning performances. Our system model sets a framework for developers of virtual classrooms and successful implementation of the model leads to students learning by interacting with their peers resulting in the construction of knowledge.

/pdf/a-web-based-virtual-classroom-system-model-53oq4zuczm.pdf

Boniface Kayode Alese

Papers

Utilization of Duckweed (Lemna paucicostata) in Least-cost Feed Formulation for Broiler Starter: A Linear Programming Analysis

A review of game theory approach to cyber security risk management

Development of Models for Managing Network Congestion on Global System for Mobile Communication (GSM) in Nigeria

Risk analysis in cyber situation awareness using Bayesian approach

A web-based virtual classroom system model